{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "skilled-still",
   "metadata": {},
   "source": [
    "# Detecting outliers using the rolling mean and median\n",
    "\n",
    "[Feature Engineering for Time Series Forecasting](https://www.trainindata.com/p/feature-engineering-for-forecasting)\n",
    "\n",
    "In this notebook we show how to identify and impute outliers in time series using the rolling mean and rolling median.\n",
    "\n",
    "We will work with a monthly retail sales dataset (found [here](https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv)).\n",
    "\n",
    "For instructions on how to download, prepare, and store the dataset, refer to notebook number 1, in the folder \"01-Datasets\" from this repo.\n",
    "\n",
    "## Data Set Synopsis\n",
    "\n",
    "The timeseries is between January 1992 and Apr 2005.\n",
    "\n",
    "It consists of a single series of monthly values representing sales volumes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "rough-exposure",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from statsmodels.tsa.seasonal import STL\n",
    "\n",
    "sns.set_context(\"talk\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "successful-consensus",
   "metadata": {},
   "source": [
    "# Identifying outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forced-architecture",
   "metadata": {},
   "source": [
    "In this notebook an outlier is a particular observation which is considered to be very different to the rest of the data. We present various methods below which define \"very different\" in a quantitative way.  We care about identifying and understanding if there are outliers in our data because they can bias forecasting models resulting in poor forecasts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "after-newman",
   "metadata": {},
   "source": [
    "For the purpose of pre-processing your data the first thing to do would be to plot your data and visually inspect for obvious outliers. Secondly, if you have a large number of time series or suspected outliers you may wish to identify them in an automated fashion. We demonstrate a range of methods below which can help to automatically identify outliers in time series data, further information about these methods can be found in this [paper [1]](https://arxiv.org/abs/2002.04236)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "announced-runner",
   "metadata": {},
   "source": [
    "Once an outlier is identified there are a set of choices that can be made depending on the nature of the outlier:\n",
    "\n",
    "1. Leave outliers in the dataset and try to use forecasting methods robust to outliers. \n",
    "2. Remove the outliers and impute them with more sensible values (e.g., using the estimation methods below or missing value imputation methods discussed in the previous lecture). This would be sensible if the outlier is known to be the result of a recording error or an event that is unlikely to repeat itself in the forecast window.\n",
    "3. Model the outlier as a feature if the nature of the outlier is known. For example, an outlier due to a public holiday can be captured by a binary feature that is 1 on the public holiday and 0 otherwise. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "direct-forestry",
   "metadata": {},
   "source": [
    "[1] Blázquez-García, Ane, et al. \"A review on outlier/anomaly detection in time series data.\" arXiv preprint arXiv:2002.04236 (2020) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unlikely-morrison",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "equipped-large",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load retail sales dataset with the artificially added outliers\n",
    "df = pd.read_csv(\n",
    "    \"../Datasets/example_retail_sales_with_outliers.csv\",\n",
    "    parse_dates=[\"ds\"],\n",
    "    index_col=[\"ds\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "basic-forest",
   "metadata": {},
   "source": [
    "# Plot the data to visually inspect any outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "precious-budget",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[10, 5])\n",
    "df.plot(y=\"y\", marker=\".\", figsize=[10, 5], legend=None, ax=ax)\n",
    "ax.set_title(\"Retail Sales with outliers\")\n",
    "ax.set_ylabel(\"Retail Sales\")\n",
    "ax.set_xlabel(\"Time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bibliographic-miracle",
   "metadata": {},
   "source": [
    "The seasonal spikes in the data are likely to be picked up as outliers. We shall de-seasonalise the data first using STL decomposition as shown in the previous notebook. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "anticipated-partition",
   "metadata": {},
   "source": [
    "# De-seasonalise data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advisory-meeting",
   "metadata": {},
   "source": [
    "A large value for the `seasonal` parameter is chosen for the STL decomposition, this is based on an assumption that the seasonality is not changing very much. In addition we set the `robust` parameter to True which implements an outlier robust version of STL decomposition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "signal-shaft",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ds\n",
       "1992-01-01   -18117.495889\n",
       "1992-02-01   -19115.124261\n",
       "1992-03-01    -5472.704639\n",
       "1992-04-01     -934.499381\n",
       "1992-05-01     2910.868481\n",
       "Name: season, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply STL decomposition\n",
    "res = STL(df[\"y\"], robust=True).fit()\n",
    "seasonal_component = res.seasonal\n",
    "seasonal_component.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "harmful-brush",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_deseasoned = (df[\"y\"] - seasonal_component).to_frame(\"y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "reported-projection",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>164493.495889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-01</th>\n",
       "      <td>166194.124261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-01</th>\n",
       "      <td>164808.704639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-01</th>\n",
       "      <td>164603.499381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-01</th>\n",
       "      <td>167157.131519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        y\n",
       "ds                       \n",
       "1992-01-01  164493.495889\n",
       "1992-02-01  166194.124261\n",
       "1992-03-01  164808.704639\n",
       "1992-04-01  164603.499381\n",
       "1992-05-01  167157.131519"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_deseasoned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "graphic-republic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[10, 5])\n",
    "\n",
    "df_deseasoned.plot(y=\"y\", marker=\".\", figsize=[10, 5], legend=None, ax=ax)\n",
    "ax.set_title(\"Retail Sales deseasonalised with outliers\")\n",
    "ax.set_ylabel(\"Retail Sales deseasonalised\")\n",
    "ax.set_xlabel(\"Time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "impaired-bosnia",
   "metadata": {},
   "source": [
    "# Estimation methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "traditional-truth",
   "metadata": {},
   "source": [
    "In this section we implement methods which identify outliers using the following criteria: an observation is an outlier if it deviates significantly from an expected value. This can be expressed as:\n",
    "\n",
    "$$|y_t - \\hat{y}_t| > \\delta$$\n",
    "\n",
    "where $y_t$ is the observation at time $t$, $\\hat{y}_t$ is the expected value at time $t$, and $\\delta$ is a user specified threshold or can be calculated from the data and change with time depending on the method. Estimation methods use values both before and after the time point $t$ to calculate $\\hat{y}_t$. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "horizontal-regulation",
   "metadata": {},
   "source": [
    "We now show how to use the rolling mean and rolling median to compute an expcted value $\\hat{y}_t$ and heuristics to setting a threshold $\\delta$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "removable-roots",
   "metadata": {},
   "source": [
    "# Rolling mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "functional-generation",
   "metadata": {},
   "source": [
    "The expected value $\\hat{y}_t$ is computed by taking a windowed mean of the observations around $t$:\n",
    "\n",
    "$$\\hat{y}_t = mean({y_{t-T}, ..., y_{t-1}, y_{t}, y_{t+1}, ..., y_{t+T}})$$\n",
    "\n",
    "where the window is of size $2T+1$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "characteristic-annotation",
   "metadata": {},
   "source": [
    "The threshold is typically some number of the windowed standard deviation:\n",
    "\n",
    "$$\\delta_t = \\alpha \\times std({y_{t-T}, ..., y_{t-1}, y_{t}, y_{t+1}, ..., y_{t+T}})$$\n",
    "\n",
    "$\\alpha=3$ is a typical choice, however this can be adjusted depending on how sensitive you want the outlier detection to be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "clean-accessory",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ = df_deseasoned.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "turkish-utility",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>rolling_mean</th>\n",
       "      <th>rolling_std</th>\n",
       "      <th>is_outlier</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>164493.495889</td>\n",
       "      <td>165890.339378</td>\n",
       "      <td>1251.760952</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-01</th>\n",
       "      <td>166194.124261</td>\n",
       "      <td>166261.547409</td>\n",
       "      <td>1563.785489</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-01</th>\n",
       "      <td>164808.704639</td>\n",
       "      <td>166537.395234</td>\n",
       "      <td>1680.647235</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-01</th>\n",
       "      <td>164603.499381</td>\n",
       "      <td>167101.829539</td>\n",
       "      <td>2386.753924</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-01</th>\n",
       "      <td>167157.131519</td>\n",
       "      <td>167368.424801</td>\n",
       "      <td>2430.789631</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        y   rolling_mean  rolling_std  is_outlier\n",
       "ds                                                               \n",
       "1992-01-01  164493.495889  165890.339378  1251.760952       False\n",
       "1992-02-01  166194.124261  166261.547409  1563.785489       False\n",
       "1992-03-01  164808.704639  166537.395234  1680.647235       False\n",
       "1992-04-01  164603.499381  167101.829539  2386.753924       False\n",
       "1992-05-01  167157.131519  167368.424801  2430.789631       False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute yhat using a rolling mean and the rolling standard deviation which will be used as\n",
    "# part of the threshold\n",
    "df_rolling_stats = (\n",
    "    df_[\"y\"]\n",
    "    .rolling(\n",
    "        window=13,  # A window of 13 is chosen to average over yearly seasonality\n",
    "        center=True,  # Use a centered window for the mean\n",
    "        min_periods=1,\n",
    "    )  # Min periods set to 1 so that edge cases also have estimates\n",
    "    .agg({\"rolling_mean\": \"mean\", \"rolling_std\": \"std\"})\n",
    ")\n",
    "\n",
    "\n",
    "df_[[\"rolling_mean\", \"rolling_std\"]] = df_rolling_stats\n",
    "\n",
    "# Apply the threshold criteria to identify an outlier\n",
    "factor = 3\n",
    "df_[\"is_outlier\"] = np.abs(df_[\"y\"] - df_[\"rolling_mean\"]) > factor * df_[\"rolling_std\"]\n",
    "\n",
    "df_.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "detected-celtic",
   "metadata": {},
   "source": [
    "Let's plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "anticipated-woman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the upper and lower boundary of the threshold for plotting\n",
    "df_[\"upper\"] = df_[\"rolling_mean\"] + factor * df_[\"rolling_std\"]\n",
    "df_[\"lower\"] = df_[\"rolling_mean\"] - factor * df_[\"rolling_std\"]\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=[10, 5])\n",
    "df_.plot(y=[\"y\", \"rolling_mean\"], marker=\".\", ax=ax)\n",
    "df_.plot(\n",
    "    y=[\"upper\", \"lower\"], figsize=[10, 5], ax=ax, color=\"k\", alpha=0.2, legend=None\n",
    ")\n",
    "\n",
    "# If any data points are identified as outlier, plot them\n",
    "if df_[\"is_outlier\"].any():\n",
    "    df_[\"y\"].loc[df_[\"is_outlier\"]].plot(\n",
    "        marker=\"o\", color=\"r\", ax=ax, legend=None, linestyle=\"\"\n",
    "    )\n",
    "    \n",
    "ax.set_title(\"Retail Sales deseasonalised with outliers\")\n",
    "ax.set_ylabel(\"Retail Sales deseasonalised\")\n",
    "ax.set_xlabel(\"Time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "applied-savannah",
   "metadata": {},
   "source": [
    "The outliers were only just identified. The rolling mean and rolling standard deviation change significantly when the window includes the outliers (see the jumps in the rolling mean and in the thresholds shown by the grey lines).  See this for yourself by adjusting the `window` parameter between small and large values. This shows that this method is not robust to outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "animal-official",
   "metadata": {},
   "source": [
    "To overcome this we will introduce the rolling median method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "potential-craps",
   "metadata": {},
   "source": [
    "# Rolling median"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exempt-thriller",
   "metadata": {},
   "source": [
    "## The Median Absolute Deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "illegal-roommate",
   "metadata": {},
   "source": [
    "The median can be used instead of the mean to provide an outlier robust value for the expected value $\\hat{y}_t$. However, we require an outlier robust estimator of the dispersion of a dataset to take the place of the standard deviation. The median absolute deviation, $MAD$, is an outlier robust estimator of the dispersion of a dataset. The $MAD$ is defined as:\n",
    "$$ MAD = median(|y - median(y)|) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vanilla-synthesis",
   "metadata": {},
   "source": [
    "We write a helper function below to compute the $MAD$ using Numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "random-april",
   "metadata": {},
   "outputs": [],
   "source": [
    "median_absolute_deviation = lambda y: np.median(np.abs(y - np.median(y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "gorgeous-payment",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: [1, 1, 1, 2, 2, 2, 1000000.0]\n",
      "Median: 2.0\n",
      "Median absolute deviation: 1.0\n",
      "Mean: 142858.42857142858\n",
      "Standard deviation: 349926.5812215296\n"
     ]
    }
   ],
   "source": [
    "# Example with an outlier\n",
    "data_with_outlier = [1, 1, 1, 2, 2, 2, 1e6]\n",
    "\n",
    "print(f\"Dataset: {data_with_outlier}\")\n",
    "print(f\"Median: {np.median(data_with_outlier)}\")\n",
    "print(f\"Median absolute deviation: {median_absolute_deviation(data_with_outlier)}\")\n",
    "print(f\"Mean: {np.mean(data_with_outlier)}\")\n",
    "print(f\"Standard deviation: {np.std(data_with_outlier)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mature-affairs",
   "metadata": {},
   "source": [
    "As you can see, the median and MAD are robust to outliers whereas the mean and standard deviation are not."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "upper-rabbit",
   "metadata": {},
   "source": [
    "## Identifying outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "homeless-vietnam",
   "metadata": {},
   "source": [
    "For this method, the expected value $\\hat{y}_t$ is computed by taking a windowed median of the observations around $t$:\n",
    "\n",
    "$$\\hat{y}_t = median({y_{t-T}, ..., y_{t-1}, y_{t}, y_{t+1}, ..., y_{t+T}})$$\n",
    "\n",
    "where the window is of size $2T+1$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "generous-framework",
   "metadata": {},
   "source": [
    "The threshold is typically some number of the windowed $MAD$:\n",
    "\n",
    "$$\\delta_t = \\alpha \\times MAD({y_{t-T}, ..., y_{t-1}, y_{t}, y_{t+1}, ..., y_{t+T}})$$\n",
    "\n",
    "$\\alpha=3.5$ is a recommended choice [2], however this can be adjusted depending on how sensitive you want the outlier detection to be.\n",
    "\n",
    "[2] Boris Iglewicz and David Hoaglin (1993), \"Volume 16: How to Detect and Handle Outliers\", The ASQC Basic References in Quality Control: Statistical Techniques, Edward F. Mykytka, Ph.D., Editor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "median-essence",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ = df_deseasoned.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "involved-logan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>rolling_median</th>\n",
       "      <th>rolling_MAD</th>\n",
       "      <th>is_outlier</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>164493.495889</td>\n",
       "      <td>166194.124261</td>\n",
       "      <td>1302.937367</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-01</th>\n",
       "      <td>166194.124261</td>\n",
       "      <td>166336.241297</td>\n",
       "      <td>1344.178495</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-01</th>\n",
       "      <td>164808.704639</td>\n",
       "      <td>166478.358333</td>\n",
       "      <td>1669.653694</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-01</th>\n",
       "      <td>164603.499381</td>\n",
       "      <td>166817.744926</td>\n",
       "      <td>1967.736600</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-01</th>\n",
       "      <td>167157.131519</td>\n",
       "      <td>167157.131519</td>\n",
       "      <td>1702.872104</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        y  rolling_median  rolling_MAD  is_outlier\n",
       "ds                                                                \n",
       "1992-01-01  164493.495889   166194.124261  1302.937367       False\n",
       "1992-02-01  166194.124261   166336.241297  1344.178495       False\n",
       "1992-03-01  164808.704639   166478.358333  1669.653694       False\n",
       "1992-04-01  164603.499381   166817.744926  1967.736600       False\n",
       "1992-05-01  167157.131519   167157.131519  1702.872104       False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rolling_stats = (\n",
    "    df_[\"y\"]\n",
    "    .rolling(\n",
    "        window=13,  # A window of 13 is chosen to average over yearly seasonality\n",
    "        center=True,  # Use a centered window for the mean\n",
    "        min_periods=1,\n",
    "    )  # Min periods set to 1 so that edge cases also have estimates\n",
    "    .agg({\"rolling_median\": \"median\", \"rolling_MAD\": median_absolute_deviation})\n",
    ")\n",
    "\n",
    "df_[[\"rolling_median\", \"rolling_MAD\"]] = df_rolling_stats\n",
    "\n",
    "# Apply the threshold criteria to identify an outlier\n",
    "factor = 3.5\n",
    "df_[\"is_outlier\"] = (\n",
    "    np.abs(df_[\"y\"] - df_[\"rolling_median\"]) > factor * df_[\"rolling_MAD\"]\n",
    ")\n",
    "\n",
    "df_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "laughing-finish",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the upper and lower boundary of the threshold for plotting\n",
    "df_[\"upper\"] = df_[\"rolling_median\"] + factor * df_[\"rolling_MAD\"]\n",
    "df_[\"lower\"] = df_[\"rolling_median\"] - factor * df_[\"rolling_MAD\"]\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=[10, 5])\n",
    "df_.plot(y=[\"y\", \"rolling_median\"], marker=\".\", ax=ax)\n",
    "df_.plot(\n",
    "    y=[\"upper\", \"lower\"], figsize=[10, 5], ax=ax, color=\"k\", alpha=0.2, legend=None\n",
    ")\n",
    "\n",
    "# If any data points are identified as outliers, plot them\n",
    "if df_[\"is_outlier\"].any():\n",
    "    df_[\"y\"].loc[df_[\"is_outlier\"]].plot(\n",
    "        marker=\"o\", color=\"r\", ax=ax, legend=None, linestyle=\"\"\n",
    "    )\n",
    "    \n",
    "ax.set_title(\"Retail Sales deseasonalised with outliers\")\n",
    "ax.set_ylabel(\"Retail Sales deseasonalised\")\n",
    "ax.set_xlabel(\"Time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "coordinated-ideal",
   "metadata": {},
   "source": [
    "As we can see the median and the MAD are more robust to outliers, there is not sudden change in the rolling median or MAD. We note both the MAD and the standard deviation will be proportional to the steepness of the trend. So the method may be more sensitive in areas with flatter trend: as we can see a smaller fluctuation in the data is identified as an outlier. A simple solution here would be to adjust the threshold. See this for yourself by increasing the `factor` variable above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alleged-family",
   "metadata": {},
   "source": [
    "# Removing the outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mature-playing",
   "metadata": {},
   "source": [
    "Once the outliers are identified and a choice is made to remove them they can be imputed using the missing value methods we introduced in the previous section of the course, or using the values from the estimation methods introduced in this notebook. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unlike-lucas",
   "metadata": {},
   "source": [
    "As an example we shall use linear interpolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "brilliant-grain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set outliers to NaN\n",
    "df_.loc[df_[\"is_outlier\"], \"y\"] = np.NaN\n",
    "\n",
    "# Apply linear interpolation\n",
    "df_.interpolate(method=\"linear\", inplace=True)\n",
    "\n",
    "# Add the seasonality extracted from STL back to deseaoned data\n",
    "df_[\"y\"] = df_[\"y\"] + res.seasonal\n",
    "\n",
    "# Plot the data and location of the identified outliers from the rolling median method\n",
    "fig, ax = plt.subplots(nrows=2, figsize=[10, 10], sharex=True)\n",
    "df.plot(y=\"y\", marker=\".\", title=\"Before imputing outliers\", ax=ax[0])\n",
    "df_.plot(y=[\"y\"], marker=\".\", title=\"After imputing outliers\", ax=ax[1])\n",
    "df_[df_[\"is_outlier\"]][\"y\"].plot(\n",
    "    marker=\"o\", color=\"r\", ax=ax[1], legend=None, linestyle=\"\", alpha=0.5\n",
    ")\n",
    "\n",
    "ax[1].set_ylabel(\"Retail Sales\")\n",
    "ax[0].set_ylabel(\"Retail Sales\")\n",
    "ax[1].set_xlabel(\"Time\")\n",
    "ax[0].set_ylim([0, 600000])\n",
    "ax[1].set_ylim([0, 600000])\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
